This research will develop parallel algorithms for machine vision, especially for the interface between image processing and image understanding, with further study of new and existing parallel architectures for efficient execution of these algorithms. Architectures to be studied include fixed-size arrays, reconfigurable meshes, reduced VLSI arrays, and arrays with hypercube connections such as the Connection Machine. Data movement techniques will be designed to support parallel solutions to image computations in mid-level and high-level vision. Specific high-level problems to be studied are motion analysis, image matching, and stereo matching, as well as several discrete relaxation techniques. Neural-net approaches to vision will be supported by design of routing techniques based on preprocessing of the underlying neural graph and by mapping of such structures onto fine-grain parallel machines. A Connection Machine at the USC Information Sciences Institute will be used to evaluate data partitioning, data routing, and mapping techniques.
